Identification apparatus and authentication system

ABSTRACT

An identification apparatus has an extraction unit comprising: an extraction unit; an acquisition unit; and an identification unit. The extraction unit extracts, from an object region in an image, a candidate region including candidate points of a predetermined object using parallax information. The acquisition unit acquires a characteristic value based on image information in the candidate region. The identification unit that identifies whether or not the candidate region includes the predetermined object based on similarity between the characteristic value and a reference characteristic value.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromthe prior Japanese Patent Application No. 2015-177676, filed on Sep. 9,2015, the entire contents of which are incorporated herein by reference.

FIELD

Embodiments of the present invention relate to an identificationapparatus and an authentication system.

BACKGROUND

Face authentication systems that automatically recognize human facesneed to extract a face region. For this purpose, a processing algorithmis conceived which extracts the face region using a positionalrelationship between the nose tip and nostrils.

In such a processing algorithm, if a distance between a face of anauthentication target and a camera is changed or the face is inclined,the accuracy of identification of the nose tip and nostrils maydeteriorate, and it is not possible to perform face authenticationaccurately.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a configuration of anauthentication system according to an embodiment;

FIG. 2 is a block diagram illustrating a configuration of anidentification apparatus according to the embodiment;

FIG. 3 is a flowchart describing processes by the identificationapparatus according to the embodiment;

FIG. 4 is a flowchart describing processes by a face region estimatingunit according to the embodiment;

FIG. 5 is a diagram illustrating contours of a foreground regionaccording to the embodiment;

FIG. 6 is a flowchart describing processes by a nose tip candidatedetecting unit according to the embodiment;

FIG. 7 is a flowchart describing processes by the nose tip detectingunit according to the embodiment;

FIG. 8 is a flowchart describing processes by a first nostril candidatedetecting unit according to the embodiment;

FIG. 9 is a diagram illustrating an example of a circular separabilityfilter according to the embodiment;

FIG. 10 is a flowchart describing processes by a second nostrilcandidate detecting unit according to the embodiment;

FIG. 11 is a flowchart describing processes by a nostril pair processingunit according to the embodiment;

FIG. 12A is a diagram illustrating a triangle made up of two nostrilcandidates and an nose tip in a frontal face according to theembodiment;

FIG. 12B is a diagram illustrating a triangle made up of two nostrilcandidates and an nose tip in a tilted face according to the embodiment;

FIG. 13 is a flowchart describing identification processes by a nostrilpair detecting unit according to the embodiment; and

FIG. 14 is a diagram describing normalization of a candidate regionaccording to the embodiment.

DETAILED DESCRIPTION

Embodiments will now be explained with reference to the accompanyingdrawings.

There is provided an identification apparatus has an extraction unitcomprising: an extraction unit; an acquisition unit; and anidentification unit. The extraction unit extracts, from an image data, acandidate region including candidate points of a predetermined objectusing parallax information. The acquisition unit acquires acharacteristic value based on image information in the candidate region.The identification unit that identifies whether or not the candidateregion includes the predetermined object based on similarity between thecharacteristic value and a reference characteristic value.

An identification apparatus according to the present embodiment detectsand uses the absolute or physical (herein and after, both absolute andphysical size means real size in centimeter of a object) size of acandidate region as well as the physical distance between candidatepoints in the candidate region in an object, using parallax informationcorresponding to the object. This suppresses deterioration inidentification accuracy of the candidate region due to a variation in aposition or posture of the object.

FIG. 1 is a block diagram illustrating a configuration of anauthentication system 1. The authentication system 1 receives an imagedata of the object and authenticates whether or not the object is anobject registered beforehand. The authentication system 1 is providedwith an authentication apparatus 100 and an identification apparatus200.

The authentication apparatus 100 is provided with a camera 102, an imageinputting unit 104, a face authenticating unit 106 and a resultdisplaying unit 108. The camera 102 is constructed of two cameras andtakes an image data of an object to be authenticated, that is, the faceof a person whose image data is taken. The distance between lenses ofthe two cameras is a base length. It is possible to obtainthree-dimensional coordinate information of a target object, that is,the face based on a parallax, that is, disparity at coordinates on twobrightness (grayscale) image data captured by the cameras. Note that theparallax can be calculated based on the two brightness image data usinga general algorithm. That is, the parallax means the value based on thedistance between the corresponding pixel in the two brightness imagedata. Moreover, the camera 102 may be constructed of more than threecameras. Here, the image inputting unit 104 inputs one brightness imagedata and a parallax image data corresponding to the brightness imagedata to the identification apparatus 200. The parallax image data isconfigured by assigning a parallax value per pixel of the brightnessimage data to pixels of the corresponding parallax image data.

The face authenticating unit 106 authenticates whether or not an objectis an object registered beforehand. More specifically, the faceauthenticating unit 106 authenticates whether or not the face is a faceregistered beforehand based on position information of the nose tip andthe pair of nostrils.

The result displaying unit 108 is constructed of, for example, a monitorand displays authentication results of the face authenticating unit 106.

The identification apparatus 200 outputs identification results to theface authenticating unit 106. FIG. 2 is a block diagram illustrating aconfiguration of the identification apparatus 200. The identificationapparatus 200 is provided with a nose tip detecting unit 202 and anostril pair detecting unit 204.

The nose tip detecting unit 202 is provided with a face regionestimating unit 206, a nose tip candidate detecting unit 208, a gradientdictionary 210 and a nose tip identifying unit 212. The nose tipdetecting unit 202 outputs position information of the nose tip.

The face region estimating unit 206 estimates the face region. Morespecifically, the face region estimating unit 206 divides the brightnessimage data or parallax image data into a foreground and a backgroundusing parallax information, and estimates a region showing a valuegreater than a parallax value (threshold) as a face region. Here, pixelvalues of the parallax image data are used as the parallax information.The face region may be estimated from the parallax image data itself ormay be estimated from the brightness image data using the correspondingparallax information.

The nose tip candidate detecting unit 208 transforms two-dimensionalcoordinates in an object region in the brightness image data intothree-dimensional coordinates using the parallax information, anddetects candidate points according to the value of a curvature of athree-dimensional curved surface generated based on thethree-dimensional coordinates. More specifically, the nose tip candidatedetecting unit 208 transforms the two-dimensional coordinates of thebrightness image data corresponding to the face region into thethree-dimensional coordinates using the parallax information. The nosetip candidate detecting unit 208 then generates a three-dimensionalcurved surface based on the three-dimensional coordinates, that is, athree-dimensional curved surface corresponding to the face surface. Thenose tip candidate detecting unit 208 calculates a curvature of eachpoint on the surface based on the generated three-dimensionalcoordinates of the curved surface and detects points indicating acurvature value that satisfies a predetermined condition as candidatepoints of the nose tip.

The gradient dictionary 210 registers reference characteristic valuesbeforehand. The reference characteristic values are feature valuescalculated based on gradient values in a region including nose tips of aplurality of people, for example. The size of region is predeterminedaccording to the general size of human nose.

The nose tip identifying unit 212 identifies whether or not a regionincluding candidate points (hereinafter referred to as “candidateregion”) corresponds to a nose tip. The nose tip identifying unit 212 isprovided with a first extraction section 214, a first acquisition unit216, and a first identification unit 218.

The first extraction section 214 extracts a candidate region from thebrightness image data based on the position and parallax information ofa detected nose tip candidate. That is the first extraction section 214determines the size of a candidate region in the brightness image datausing the parallax information of a nose tip candidate and extracts thecandidate region which is centered at the nose tip candidate from thebrightness image data. In this case, the size of the candidate region isdetermined to grow larger as the parallax of the face region increases.That is, as the face approaches the camera 102, the candidate region isdetermined to become larger. Note that the size of the candidate regionis then normalized according to the general size of human nose and theparallax of the face region.

The first acquisition unit 216 obtains feature values using the imageinformation of the candidate region. That is, the first acquisition unit216 calculates the feature values using the brightness information ofthe candidate region. As a feature value, for example, a brightnessgradient feature (histogram of gradient) is calculated.

The first identification unit 218 identifies whether or not thecandidate region corresponds to a predetermined object based onsimilarity between the feature values and reference characteristicvalues registered with the gradient dictionary 210. That is, the firstidentification unit 218 identifies whether or not the candidate regioncorresponds to a nose tip based on similarity between the feature valuescalculated in the first acquisition unit 216 and the referencecharacteristic values.

For example, when there are a plurality of candidate regions, the firstidentification unit 218 identifies a candidate region showing thehighest similarity to the reference characteristic values as thecandidate region corresponding to a nose tip. That is, the firstidentification unit 218 designates the candidate point corresponding tothe identified candidate region as the nose tip. The firstidentification unit 218 then outputs the position information of thenose tip to the face authenticating unit 106.

The nostril pair detecting unit 204 outputs the position information ofthe nostril pair. The nostril pair detecting unit 204 is provided with anostril candidate detecting unit 220, a pattern dictionary 222, and anostril pair identifying unit 224. The nostril candidate detecting unit220 detects nostril candidates. The nostril candidate detecting unit 220is provided with a first nostril candidate detecting unit 226 and asecond nostril candidate detecting unit 228.

The first nostril candidate detecting unit 226 detects a nostrilcandidate based on shape information from a region defined based on theposition of the nose tip. That is, the first nostril candidate detectingunit 226 detects a region based on a circular shape as a nostrilcandidate.

The second nostril candidate detecting unit 228 extracts a region, abrightness value of which is equal to or less than a predetermined valueand a parallax of which is equal to or less than a predetermined valueas a nostril candidate from a region determined based on the position ofthe nose tip. Moreover, the second nostril candidate detecting unit 228detects nostril candidates so that the total number of nostrilcandidates reaches a predetermined number N. That is, the first nostrilcandidate detecting unit 226 detects nostril candidates first and thesecond nostril candidate detecting unit 228 detects nostril candidatescorresponding to a difference from the predetermined number N.

The pattern dictionary 222 registers reference patterns beforehand. Thereference patterns are obtained by averaging image data includingnostril pairs of a plurality of people.

The nostril pair identifying unit 224 identifies a pair of nostril fromat most N nostril candidates detected by the nostril candidate detectingunit 220. The nostril pair identifying unit 224 is provided with anostril pair processing unit 230, a second extraction section 232, asecond acquisition unit 234 and a second identification unit 236.

The nostril pair processing unit 230 pairs nostril candidates detectedby the nostril candidate detecting unit 220. The nostril pair processingunit 230 assumes two nostrils as a nostril pair candidate, if and onlyif the physical distance between the two nostril candidates and thephysical distance from nostril candidates to the nose tip are within apredetermined distance range. Note that physical distance is calculatedusing the parallax of the three points and distances between them inimage data. That is, the distance between the nostril candidates in theimage data and the distance between the nose tip and the nostrilcandidates increase as the corresponding parallax information grows.That is, the distance between nostril candidates and the distancebetween the nose tip and the nostril candidates in a three-dimensionalspace are fixed values based on the size of the nose of the person, evenwhen the corresponding parallax information changes during the capturingof image data. The predetermined distance range is defined based on thegeneral physical distances between nostrils and from nostrils to nosetip.

The second extraction section 232 extracts a candidate region based onthe nostril pair candidates. Since the distance between the nostril paircandidates in image data is determined by the parallax information, thecandidate region increases in size as the face approaches the camera102. The size of the candidate region is then normalized according tothe parallax of the face region.

The second acquisition unit 234 acquires characteristic values based onthe image information of the candidate region. That is, the secondacquisition unit 234 assumes the brightness information of the candidateregion as a template and assumes the pixel values making up the templateas characteristic values.

The second identification unit 236 identifies whether or not thecandidate region corresponds to a predetermined object based onsimilarity between this characteristic value and referencecharacteristic values. That is, the second identification unit 236identifies whether or not the candidate region corresponds to thenostril pair based on the similarity between the nostril pair candidateimage data obtained in the second acquisition unit 234 as the templateand a reference pattern. The reference pattern is a reference image datacalculated based on the nostril pair image data beforehand andregistered with the pattern dictionary 222.

For example, when there are a plurality of candidate regions, the secondidentification unit 236 identifies a candidate region showing thehighest similarity to the reference characteristic values as a candidateregion corresponding to the nostril pair. That is, the secondidentification unit 236 designates the nostril pair candidatecorresponding to the identified candidate region as the nostril pair.The second identification unit 236 then outputs the position informationcorresponding to the nostril pair to the face authenticating unit 106.

Next, operation of the identification apparatus 200 will be described.FIG. 3 is a flowchart describing processes by the identificationapparatus 200. The face region estimating unit 206 estimates a faceregion from a brightness image data (S30). That is, the face regionestimating unit 206 estimates a foreground region in the brightnessimage data whose parallax is equal to or greater than a predeterminedthreshold as the face region using the parallax information. In thiscase, when the estimated size of the face region and an aspect ratiodeviate over a predetermined range, the face region estimating unit 206determines that the region is not a face region (S32: No), and ends theentire processing. That is, when, of respective foreground regionsestimated by gradually changing the threshold, if there is no foregroundregion where the size and the aspect ratio fall within a predeterminedrange, the face region estimating unit 206 determines that there is noface region and ends the entire processing.

On the other hand, when the estimated size of the foreground region andthe aspect ratio fall within the predetermined range, the face regionestimating unit 206 determines that the foreground region is a faceregion (S32: Yes). The nose tip candidate detecting unit 208 transformstwo-dimensional coordinates of image pixels within the object region inthe brightness image data, that is, within the face region intothree-dimensional coordinates which are a group of face points using theparallax information (S34). Next, the nose tip candidate detecting unit208 performs processing such as smoothing on the group of face pointsand removing noise of the group of face points (S36). The nose tipcandidate detecting unit 208 calculates a curvature of eachthree-dimensional point in the group of face points and detects, whencurvature values at these three-dimensional points are within apredetermined range, these three-dimensional points as nose tipcandidates respectively (S38). Coordinates of the three-dimensionalpoints of each nose tip candidate are inversely transformed intocoordinates in a two-dimensional brightness image data using parallaxinformation and designated as candidate points of the nose tip in thebrightness image data.

Next, the nose tip detecting unit 202 extracts a candidate regionincluding the candidate points of the nose tip from within the faceregion and identifies whether or not the candidate region corresponds tothe nose tip (S40). In this case, the nose tip detecting unit 202calculates a feature value using image information within the candidateregion and identifies whether or not the candidate region corresponds tothe nose tip based on the similarity between this feature value andreference feature values registered with the gradient dictionary 210.That is, when the similarity between the feature value obtained from thecandidate region and the reference feature values registered with thegradient dictionary 210, that is, when a comparison score is equal to orgreater than a predetermined value, the nose tip detecting unit 202identifies that the candidate region corresponds to the nose tip. Thesize of the candidate region is determined based on an average value ofthe parallax corresponding to, for example, the face region. When theidentification result shows that there are a plurality of candidateregions identified as the nose tip, candidate points corresponding tothe candidate region having the highest comparison score, that is, thehighest similarity is designated as the nose tip.

Next, it is determined whether or not the nose tip exists (S42). When itis determined that the nose tip does not exist (S42: No), that is, whenthere is no candidate region, the comparison score of which indicates apredetermined value or greater, the entire processing is ended. On theother hand, when it is determined that the nose tip exists (S42: Yes),the nostril candidate detecting unit 220 applies a circular separationfilter to the brightness image data and detects nostril candidates(S44).

Furthermore, the nostril candidate detecting unit 220 calculates anabsolute physical distance from image pixels in a candidate region tothe nose tip using the parallax image data and adds the pixel as anostril candidate of a given shape if this absolute distance fallswithin a certain range (S46). That is, the nostril candidate detectingunit 220 extracts image pixels the brightness value of which is equal toor less than a predetermined value and a parallax of which is equal toor greater than a predetermined value from a region determined based onthe position of the nose tip as a nostril candidate.

Next, the nostril pair detecting unit 204 pairs any two arbitrarynostril candidates from all nostril candidates as nostril paircandidates (S48). In this case, the nostril pair detecting unit 204detects nostril pair candidates in which an absolute distance betweennostrils of the nostril pair candidates is within a certain range andabsolute distances between the nose tip and the respective nostrils arewithin a certain range. The absolute distance is calculated based onparallax: information corresponding to the face region.

Next, the nostril pair detecting unit 204 identifies whether or not thecandidate region corresponds to the nostril pair based on the similaritybetween characteristic values obtained from the candidate region basedon the nostril pair and the reference characteristic values registeredwith the pattern dictionary 222 (S50). That is, the nostril pairdetecting unit 204 compares the image data within the candidate regionincluding the nostril pair candidates as templates with image data ofthe reference nostril pair registered with the pattern dictionary 222,performs processing of determining the nostril pair candidate within acandidate region having the highest comparison score as a nostril pairand ends the entire processing.

Thus, since a feature value is calculated from the candidate region ofthe nose tip whose size is determined based on the parallax informationcorresponding to the face region, even when the distance between thecamera 102 and the face varies, it is possible to stably identify thenose tip. A region corresponding to the nostril pair candidates in whichan absolute distance between the pair of nostrils falls within apredetermined range is acquired as a template using parallax informationcorresponding to the face region, and it is thereby possible to stablyidentify the nostril pair even when the distance between the camera 102and the face varies. Furthermore, a region in which the brightness valueis equal to or less than a predetermined value and the parallax is equalto or greater than a predetermined value is extracted as nostrilcandidates, and it is thereby possible to extract a nostril candidateeven when the orientation of the face is tilted and the shape of thenostril is deviated from the circular shape.

Next, operation of the face region estimating unit 206 will bedescribed. FIG. 4 is a flowchart describing processes by the face regionestimating unit 206. The face region estimating unit 206 resizes aparallax image data and obtains a reduced parallax image data (S60). Asthe resizing method, for example, nearest neighbor interpolation,bilinear interpolation or an average value method is used.

Next, the face region estimating unit 206 calculates a separationthreshold using a histogram of a reduced parallax image data and removesa background of the reduced parallax image data (S62). That is, for eachpixel of the reduced parallax image data, when a pixel value, that is, aparallax value is smaller than a threshold, the face region estimatingunit 206 sets a pixel value of the pixel to 0 as the background region.A region where the pixel value is not 0 is a foreground region.

Next, the face region estimating unit 206 calculates the size of theforeground region, that is, “Size” using equation (1) (S64). In equation(1), “B” denotes a distance between lenses of the camera 102, and“Rect_width” and “Rect_height” denote the number of pixels in thehorizontal and vertical directions of the minimum rectangle surroundingthe foreground, that is, a circumscribing rectangle. Furthermore,“depth_avg” denotes a parallax average value of all pixels in theforeground region.

Size=(B×(Rect_width+Rect_(height)))/(2×depth_avg)  (1)

Sizew=(B×Rect_width)/(depth_avg)  (2)

Sizeh=(B×Rect_height)/(depth_avg)  (3)

The “Size” calculated according to equation (1) is compared with thephysical size of a known general human faces, that is, threshold T andit is determined whether or not “Size” is greater than threshold T(S66).

When “Size” is greater than threshold T (S66: Yes), it is determinedthat an object other than the face exists in the foreground region. Inthis case, the separation threshold is changed and the next backgroundseparation is performed (S68). For the changed separation threshold,“depth_avg” can be used. As the execution result in S58, the foregroundregion before the separation is divided into a plurality of connectedregions. Next, the face region estimating unit 206 extracts respectivecontours of the connected regions (S70). When a plurality of connectedregions exist, the respective contours of the connected regions areextracted.

FIG. 5 is a diagram illustrating contours of the foreground region. Asshown in FIG. 5(A), a parallax value of a parallax image data isexpressed by a pattern of light and dark (the denser the parallax value,the closer to the camera the foreground). In FIG. 5(B), there are aplurality of connected regions and contours exist in the plurality ofconnected regions.

The face region estimating unit 206 selects a face candidate region asan estimation result from among the contours (S72). In this case,absolute lengths in the horizontal direction “Sizew” and in the verticaldirection “Sizeh” in the contour region are calculated using equation(2) and equation (3). A region having a size and an aspect ratio whichare most similar to those of a human face is selected as the facecandidate region. The rectangle in FIG. 5(C) is an example of theselected face region. After the processing in S72, the flow returns toS64.

When “Size” is smaller than or equal to threshold T (S66: No), therectangle surrounding the foreground region is estimated as the faceregion and the processing in the face region estimating unit 206 endsthe processing. Thus, the foreground region is separated from thebackground region using the parallax information and the face region isestimated based on the physical size of the separated foreground region.When there are a plurality of foreground regions, the aspect ratio ofthe region is also used to estimate the foreground region most likely tobe the face as the face region.

Next, operation of the nose tip candidate detecting unit 208 will bedescribed. FIG. 6 is a flowchart describing processes by the nose tipcandidate detecting unit 208.

Here, a case will be described where a candidate point of the nose tipis detected from within the face region detected by the face regionestimating unit 206. Furthermore, a case will also be described wherethe nose tip candidate detecting unit 208 transforms the face regiondetected by the face region estimating unit 206 into three-dimension (X,Y, Z). That is, the nose tip candidate detecting unit 208 transformstwo-dimensional image pixel (Ix, Iy) (Lx<=Ix<=Rx, Ly<=Ty<=Ry) within theface region (coordinates at left top corner: (Lx, Ly), coordinates atright bottom corner: (Rx, Ry)) into three-dimension (X, Y, Z). The setof three-dimensional points generated by this transformation becomes athree-dimensional point group (point cloud) of the face region. Thistransformation can be performed using, for example, equation (4).

$\begin{matrix}{\begin{pmatrix}X \\Y \\Z \\W\end{pmatrix} = {Q\begin{pmatrix}I_{x} \\I_{y} \\{{disparity}\mspace{14mu} \left( {I_{x},I_{y}} \right)} \\1\end{pmatrix}}} & (4)\end{matrix}$

In equation (4), “Ix(Iy)” denotes coordinates on the X(Y)-axis of animage pixel in the face region, “disparity (Ix, Iy)” denotes a parallaxcorresponding to the pixel (Ix, Iy) and “(X, Y, Z)” denotes coordinatesof three-dimensional points under a world coordinate systemcorresponding to (Ix, Iy). “W” is a fixed value and “Q” is a 4×4perspective transformation matrix determined by a focal length and adistortion coefficient which are inner parameters of the camera 102.When two left and right cameras 102 are used, a rotation matrix and atranslation vector between the cameras 102 are also needed to obtain theperspective transformation matrix Q. Generally, the perspectivetransformation matrix Q can be obtained by executing a cameracalibration algorithm which is open to the public.

Here, the point group generated may be used as is. Alternatively, thepoint group generated may be processed and used. For example, the pointgroup may be subjected to processing of down-sampling and smoothing.When the point group is down-sampled, it is possible to reduce theamount of calculation required to surface-approximate the point group.Note that, surface approximation is performed for one three-dimensionalpoint in the point group, by fitting a number of three-dimensionalpoints within a range surrounding the aforementioned point to a curvedsurface. On the other hand, when the point group is smoothed, it ispossible to further reduce noise and improve the accuracy of surfaceapproximation.

First, the nose tip candidate detecting unit 208 determines the size ofa window (width and height are n) for surface approximation, so thateach surface approximated from three-dimensional point group correspondsto a partial face region, the size of which is close to human nose.(S80). A curvature corresponding to a three-dimensional point is thencalculated using a curved surface within a range defined by the windowcentered on a three-dimensional point. Here, the curved surface isapproximated using p three-dimensional points (x_(i), y_(i), z_(i))(1<=i<=p) corresponding to p pixels within a rectangular region having ahorizontal width of n and a vertical width of n in a two-dimensionalimage data.

In this case, the width “n” is set according to the actual size of thenose. That is, a number of three-dimensional curved surfaces within theface region corresponding to the actual size of the nose areapproximated. The center of a curved surface having a curvature close tohuman nose curvature is considered as nose tip candidate. The width “n”can be calculated using, for example, equation (5).

n=(fit_size×depth_avg)/B  (5)

The term “fit_size” is a fixed value and fit_size=36 mm is used here asthe size of the human nose. Furthermore, “depth_avg” is an averageparallax of the face region estimated by the face region estimating unit206. Note that when the curved surface is fitted, this may be fittedusing three-dimensional points at an interval of “s” (1≦s<n/2) in thehorizontal direction and the vertical direction for speed enhancement.In this case, the number of fitted surfaces is decreased.

Next, it is determined whether or not the processing on allthree-dimensional points in the face region has ended (S82). When theprocessing on all three-dimensional points has not ended (S82: No), amatrix for calculating a curvature from the curved surface correspondingto the next three-dimensional point (CX, CY, CZ) is generated (S84). Ifthe respective coordinates (x_(i), y_(i), z_(i)) (1<=i<=p) of pthree-dimensional points surrounding (CX, CY, CZ) within defined windoware substituted into equation (6), a matrix necessary to calculate acurved surface coefficient is generated according to equation (7). Thatis, equation (6) expresses an expression of the curved surfacecorresponding to the three-dimensional point (CX, CY, CZ).

z _(i)(x _(i) ,y _(i))=a+b×(x _(i) −CX)+c×(y _(i) −CY)+d×(x _(i) −CX)×(y_(i) −CY)+e×(x _(i) −CX)² +f×(y _(i) −CY)²  (6)

A coefficient “a” is a fixed value and coefficients “b” “c” and “d” arefirst derivatives of the three-dimensional point (CX, CY, CZ), Moreover,coefficients “e” and “f” are second derivatives of the three-dimensionalpoint (CX, CY, CZ). To obtain the above-described coefficients, equation(6) is transformed into the form of matrix calculation in equation (7).Thus, a curved surface corresponding to the three-dimensional point (CX,CY, CZ) within the face region is generated and a curvature iscalculated.

A×X=Z  (7)

Matrix A is a matrix on the left side of equation (8) and a columnvector Z is the right side of equation (8).

             (8) ${\begin{pmatrix}1 & {x_{1} - {CX}} & {y_{1} - {CX}} & {\left( {x_{1} - {CX}} \right)\left( {y_{1} - {CY}} \right)} & \left( {x_{1} - {CX}} \right)^{2} & \left( {y_{1} - {CY}} \right)^{2} \\1 & {x_{2} - {CX}} & {y_{2} - {CX}} & {\left( {x_{2} - {CX}} \right)\left( {y_{2} - {CY}} \right)} & \left( {x_{2} - {CX}} \right)^{2} & \left( {y_{2} - {CY}} \right)^{2} \\\vdots & \vdots & \vdots & \vdots & \vdots & \vdots \\1 & {x_{p} - {CX}} & {y_{p} - {CX}} & {\left( {x_{p} - {CX}} \right)\left( {y_{p} - {CY}} \right)} & \left( {x_{p} - {CX}} \right)^{2} & \left( {y_{p} - {CY}} \right)^{2}\end{pmatrix}\begin{pmatrix}a \\b \\c \\d \\e \\f\end{pmatrix}} = \begin{pmatrix}z_{1} \\z_{2} \\\vdots \\z_{p}\end{pmatrix}$

Next, the curved surface coefficients are calculated according toequation (8) (S86). Since coordinates (x_(i), y_(i), z_(i)) (1<=i<=p) ofthe respective three-dimensional points and coordinates of thethree-dimensional point (CX, CY, CZ) which is the center are known,X=vector (a, b, c, d, e, f)^(T) can be calculated.

Next, a mean curvature H and a Gaussian curvature K of thethree-dimensional point (CX, CY, CZ) are calculated using the curvedsurface coefficients {a, b, c, d, e, f} (S88). The mean curvature andthe Gaussian curvature are calculated according to equation (9) andequation (10) respectively.

H=[(1+c ²)×e−b×c×d+(1+b ²)×f]/(1+b ² +c ²)^(1.5)  (9)

K=(4×e×f−d ²)/(1+b ² +c ²)²  (10)

Next, it is determined whether the mean curvature H and Gaussiancurvature K are within a range of a predetermined value using equation(11) (S90).

H<T _(H)(T _(H)≦0),K≧T _(K)(T _(K)>0)  (11)

When both the mean curvature H and the Gaussian curvature K are within arange of a predetermined value (S90: Yes), candidate points are detectedby assuming the three-dimensional point (CX, CY, CZ) as a candidatepoint of the nose tip (S92), and the flow returns to the process in S82.With the nose tip candidate detected according to equation (11), themean curvature value is smaller than 0 and the Gaussian curvature isgreater than 0, and therefore the nose tip is always a convex. On theother hand, when both the mean curvature H and the Gaussian curvature Kare not within the range of a predetermined value (S90: No), the flowreturns to the process in S82. When processing on all three-dimensionalpoints in the face region ends (S82: Yes), the entire processing isended.

In this way, the nose tip candidate detecting unit 208 calculates themean curvature H and the Gaussian curvature K for all three-dimensionalpoints within the face region. When the mean curvature H and theGaussian curvature K satisfy predetermined conditions, thethree-dimensional points are outputted as candidate points of the nosetip.

Next, operation of the nose tip detecting unit 202 will be described.FIG. 7 is a flowchart describing processes by the nose tip detectingunit 202. The nose tip detecting unit 202 first inputs coordinates ofcandidate points detected by the nose tip candidate detecting unit 208(S100).

Next, the nose tip detecting unit 202 determines whether or notprocessing on all the inputted candidate points has ended (S102). Whenprocessing on all the inputted candidate points has not ended (S102:No), the first extraction section 214 transforms three-dimensionalcoordinates of candidate points to be the next processing targets intotwo-dimensional coordinates in a brightness image data using parallaxinformation (S104). That is, the first extraction section 214 transformsthe candidate point (CX, CY, CZ) of the nose tip into coordinates on animage plane. In this case, a candidate point (OX, OY) of the nose tip iscalculated as two-dimensional coordinates on a brightness image data atthe candidate point (CX, CY, CZ) using the Q matrix in equation (4).

Next, the first extraction section 214 extracts a candidate regionwithin the brightness image data centered on the nose tip candidatepoint (OX, OY) using equation (5) (S106). That is, the lengths in thehorizontal and vertical directions of the candidate region arecalculated by substituting parallax values at the (OX, OY) coordinatesinto “depth_avg” in equation (5).

Next, the first extraction section 214 normalizes the brightness imagedata within the candidate region into a certain size (S108).Normalization can be processed using, for example, bilinearinterpolation. Here, normalization is processed assuming the size of thebrightness image data of the nose within the normalized candidate regionas 60×60.

Next, the first acquisition unit 216 calculates a brightness gradientHOG (histogram of gradient) feature of the brightness image data withinthe normalized candidate region (S110). When the HOG feature iscalculated, for example, the image data is divided into cells of 6×6 andone cell is divided into blocks of 3×3. Regarding gradient directions, ahistogram with gradients in a total of 9 directions in 20-degreeincrements from 0 to 180 degrees is statistically processed. For thisreason, a vector of 3×3×9=81 dimensions is obtained for one block. Thus,a vector of 6561 dimensions is calculated from image data within onecandidate region as a feature value.

Next, the first identification unit 218 calculates a score indicatingsimilarity between the calculated feature value and reference featurevalues registered with the gradient dictionary 210 beforehand (S112).That is, the first identification unit 218 calculates a score ofcomparison between the calculated feature value and the referencefeature values. Then, the flow returns to the process in S102 and whenprocessing on all candidate points is ended (S102: Yes), a candidatepoint corresponding to a candidate region having the highest comparisonscore is identified as the nose tip (S114). The coordinates of this nosetip are outputted as the detection result (S116), and the entireprocessing is ended. Thus, the feature value is calculated based onimage information within the candidate region whose size is determinedusing parallax information and it is identified whether or not thecandidate region corresponds to the nose tip based on the similaritywith the reference feature values.

For example, a support vector machine (SVM) is used to learn the featurevalue here and an identifier is configured (S118). That is, theprocesses in S104 to S110 are performed on a plurality of candidatepoints whose identification results are known, to calculate a brightnessgradient HOG feature accompanied by the identification result as afeature value. Next, the identifier is configured using the supportvector machine for these feature values. Thus, it is possible to assignan identification score to a feature value in an unknown category. Thatis, this identification score shows similarity with a feature valueobtained from the candidate region including the nose tip.

For example, this identifier is designed to learn feature values byassigning “1” to a feature value obtained from a candidate regionincluding the nose tip and assigning “−1” to a feature value obtainedfrom a candidate region not including the nose tip. In this case, as avalue closer to “1” is assigned to an unknown feature value, thepossibility corresponding to the nose tip increases. On the other hand,as a value closer to “−1” is assigned, the possibility corresponding tothe nose tip decreases. That is, the higher the similarity between afeature value in an unknown category and a feature value obtained from acandidate region including the nose tip, the closer to “1” is the valueassigned to the unknown feature value.

Next, operation of the first nostril candidate detecting unit 226 willbe described. FIG. 8 is a flowchart describing processes by the firstnostril candidate detecting unit 226. A parallax image data togetherwith brightness image data is inputted to the first nostril candidatedetecting unit 226 (S140).

Next, the first nostril candidate detecting unit 226 extracts acandidate region from within the brightness image data centered on thenose tip (OX, OY) using equation (5) and normalizes the candidate regioninto a certain size (S142). Normalization can be processed using, forexample, bilinear interpolation. Here, such processing is performedassuming the size of the normalized candidate region is 60×60. That is,the lengths in the horizontal and vertical directions of the candidateregion are calculated by substituting parallax values at the (OX, OY)coordinates into “depth_avg” in equation (5). Since normalization isperformed using parallax information at the nose tip position, theparallax image data can be obtained in a fixed size.

Here, a circular separability filter will be described. FIG. 9 is adiagram illustrating an example of the circular separability filter. Thecircular separation filter is a filter that defines two circularregions. That is, the circular separability filter is constructed ofconcentric circles “Region1” and “Region2” having different radiuses r1and r2 respectively. The degree of separation of brightness valuesbetween “Region1” and “Region2” is calculated. When a certain region isdivided into two regions, the degree of separation is a proportion of abrightness variation between the regions in a brightness variation of auniversal region. The degree of separation takes a maximum value of 1.0when the two regions are completely separated and approaches a minimumvalue of 0 when the two regions are not separated. The circularseparability filter exhibits a high value in the vicinity of thecircular nostrils and has a characteristic of being not susceptible toillumination variations or noise.

The first nostril candidate detecting unit 226 sets a search region towhich the circular separability filter is applied (S144). The nostrilsmay be located at various positions with respect to the nose tip. Forthis reason, all brightness image data centered on the nose tip may besearched. Alternatively, when great importance is placed on theprocessing amount, the search range may be determined depending on thedistance from the nose tip. That is, based on the physical distance fromthe nose tip, regions where the nostrils are less likely to exist may beremoved from the search region.

Next, the first nostril candidate detecting unit 226 sets a radius ofthe circular separation filter (S146). Outer/inner radiuses of thefilter can be set according to the size of a normalized nose image datarespectively.

Next, the first nostril candidate detecting unit 226 applies circularseparability filter to the normalized nose image data (S148). Throughthis processing, values as a result of conducting circular separabilityfilter are obtained in correspondence with each pixel of the normalizednose image data. Here, an image data obtained by assigning the values ofthe circular separability filter to the respective pixels of thenormalized nose image data is assumed to be a result image data.

Next, the first nostril candidate detecting unit 226 designates pixelsin the result image data that exceed a predetermined value as nostrilcandidates (S150). In this case, regions exceeding a predetermined valuemay be subjected to labeling processing or the like and typical pointsin each region to which a label is assigned may be assumed to be nostrilcandidates.

Next, operation of the second nostril candidate detecting unit 228 willbe described. FIG. 10 is a flowchart describing processes by the secondnostril candidate detecting unit 228. Here, extraction of non-circularnostril candidates will be described. When the face is orienteddiagonally, nostril candidates having a shape different from a circleneed to be detected.

A parallax image data is inputted to the second nostril candidatedetecting unit 228 (S160). Next, the second nostril candidate detectingunit 228 calculates the minimum brightness value K in a brightness noseimage data (S162), and then calculates an average parallax of theparallax image data (S164).

Next, the second nostril candidate detecting unit 228 reads pixels inthe brightness image data as processing targets (S166). The secondnostril candidate detecting unit 228 then determines whether or notprocessing on all pixels in the brightness image data has ended (S168).When the processing on all pixels in the brightness image data has ended(S166: Yes), the entire processing is ended.

On the other hand, when the processing on all pixels has not ended(S168: No), the second nostril candidate detecting unit 228 determineswhether or not the brightness value of the pixel is less than r timesthe minimum brightness value K (S170). Since the brightness values ofthe nostrils exhibit values lower than pixels around the nostril, r canbe set so as to correspond to the brightness values of the nostrils. Thevalue of r here is set to a value between 1.0 to 1.5, for example.

When the brightness value of a pixel is not less than a brightness valueK·r (S170: No), the process on the pixel is ended and the flow returnsto the process in S166. On the other hand, when the brightness value ofa pixel is less than a brightness value K·r (S170: Yes), it isdetermined whether or not the parallax value of the pixel is higher thanan average parallax (S172). When the parallax value of the pixel isequal to or less than an average parallax (S172: No), the process on thepixel is ended and the flow returns to the process in S166. On the otherhand, when the parallax value of the pixel is greater than the averageparallax (S172: Yes), an absolute distance (distance in thethree-dimensional space) between this pixel and the nose tip, that is, adistance from the center of the image data is calculated using equation(12) (S174).

“Lengh” in equation (12) is an absolute distance between the nose tipand the pixel, “d1” is a parallax of the nose tip, “d2” is a parallax ofthe pixel to be processed.

D=2×B×Lengh/(d1+d2)  (12)

Next, it is determined whether or not the absolute distance D is greaterthan threshold D1 and smaller than threshold D2 (S176). Here, thedetection range of nostril candidates is restricted by the absolutedistance to the nose tip. That is, D1 and D2 are set based on thedistance between the actual human nostrils and nose tip, and forexample, values of D1=0.6 cm, D2=2.0 cm can be used.

When the absolute distance D is equal to or less than D1 or equal to orgreater than D2 (S176: No), the process on the pixel is ended and theflow returns to the process in S166. On the other hand, when theabsolute distance D is greater than D1 and smaller than D2 (S176: Yes),it is determined whether or not the number of already selectedcandidates satisfies N (S178). When the number of already selectedcandidates does not satisfy N (S178: No), this pixel is added to thenose candidates (S180) and the flow returns to the process in S166.

On the other hand, when the number of already selected candidatessatisfies N (S178: Yes), the candidates are not added as nose candidatesand the entire processing is ended. When the amount of processing neednot be considered important, the number of candidates N may be set tothe maximum number of pixels so as to process all pixels.

Next, operation of the nostril pair detecting unit 204 will bedescribed. FIG. 11 is a flowchart describing processes by the nostrilpair processing unit 230. Here, a case will be described where it isdetermined whether or not a combination of two nostrils satisfies anostril pair candidate condition. The number of combinations of nostrilsat most is N×(N−1)/2 since the nostril candidate detecting unit 220detects N nostril candidates. That is, a case will be described wherethese combinations of nostrils are sequentially inputted to andprocessed in the nostril pair processing unit 230.

The nostril pair processing unit 230 inputs a combination of a nostrilcandidate 1 and a nostril candidate 2 (S190). Next, the nostril pairprocessing unit 230 calculates an absolute distance between the nostrilcandidate 1 and the nose tip using equation (12). Similarly, the nostrilpair processing unit 230 calculates an absolute distance between thenostril candidate 2 and the nose tip (S192).

Next, the nostril pair processing unit 230 determines whether or not thedistance between the nostril candidate 1 and the nose tip, and thedistance between the nostril candidate 2 and the nose tip are greaterthan D1 and less than D2 respectively (S194). When the respectivedistances are equal to or less than D1 or equal to or greater than D2(S194: No), the combination determination process is ended. On the otherhand, when the respective distances are greater than D1 and less than D2(S194: Yes), the absolute distance between the nostril candidate 1 andthe nostril candidate 2 is calculated using equation (12) (S196). Inthis case, d1 is assumed to be the parallax of the nostril candidate 1and d2 is assumed to be the parallax of the nostril candidate 2.

Next, it is determined whether or not the distance between the nostrilcandidate 1 and the nostril candidate 2 is greater than 2*D1 and lessthan D2. (S198). When the distance between the nostril candidate 1 andthe nostril candidate 2 is equal to or less than 2*D1 or greater than D2(S198: No), this combination determination process is ended.

On the other hand, when the distance between the nostril candidate 1 andthe nostril candidate 2 is greater than 2*D1 and less than D2 (S198:Yes), a cosine value of a triangle made up of the nostril candidate 1,the nostril candidate 2 and the nose tip is calculated (S200).

Here, the triangle made up of two nostril candidates and the nose tipwill be described. FIG. 12A is a diagram illustrating a triangle made upof two nostril candidates and the nose tip on a frontal face. FIG. 12Bis a diagram illustrating a triangle made up of two nostril candidatesand the nose tip on a tilted face. With the nose tip and the left andright nostrils of a human nose, angles A1 and A2 located at thepositions of the nostril candidates range 0 to 90 degrees no matter whatis the orientation of the face.

It is determined whether values of cos(A1) and cos(A2) are greater than0 (S202). When the value of cos(A1) is equal to or less than 0 or thevalue of cos(A2) is equal to or less than 0 (S202: No), the process onthis combination is ended. On the other hand, when the values of cos(A1)and cos(A2) are greater than 0 (S202: Yes), the nostril candidate 1 andthe nostril candidate 2 are assumed to be nostril pair candidates(S204), and the process on this combination is ended. In this way,nostril pair candidates are selected from among N×(N−1)/2 combinationsof nostrils.

Next, operation of the nostril pair detecting unit 204 will bedescribed. FIG. 13 is a flowchart describing identification processes bythe nostril pair detecting unit 204. Here, a process will be describedwhich identifies whether or not a nostril pair candidate selected by thenostril pair processing unit 230 is a nostril pair.

The nostril pair detecting unit 204 inputs a nostril pair candidate(S210). Next, the second extraction section 232 extracts a rectangleregion surrounding the nostril pair candidate as a candidate region andnormalizes the image data in the candidate region (S212).

Here, this normalization will be described. FIG. 14 is a diagramdescribing normalization of a candidate region. First, a nose image datais rotated and a straight line connecting the nose tip and the centerpoint of the nostril pair candidate is placed in a vertical position. Inthis case, the nose image data is rotated so that the nose tip islocated above and the nostril pair candidate is located below the nosetip. Next, the image data is rotated again and the nostril paircandidate is put in a horizontal position. Assuming a midpoint of thenostril pair candidate arranged horizontally as the center, a rectangleregion surrounding the nostril pair candidate is assumed to be acandidate region. Next, the second acquisition unit 234 resizes theimage data within the candidate region to a fixed size and calculates anormalized image data as a template.

The second identification unit 236 calculates a comparison score showingsimilarity between the template and the reference image data registeredwith the pattern dictionary 222 (S214). Here, for example, anormalization correlation value between the template and the referenceimage data is calculated as a comparison score.

Next, the nostril pair detecting unit 204 determines whether or notprocessing on all nostril pair candidates is completed (S216). Whenprocessing on all nostril pair candidates is not completed (S216: No),the nostril pair detecting unit 204 returns to S210.

On the other hand, when processing on all nostril pair candidates iscompleted (S216: Yes), the second identification unit 236 merges, basedon the X and Y coordinates of the nostril pair, the nostril paircandidates whose X and Y coordinates are to close to each other into onenostril candidate (S218). This makes it possible to reduce the number ofnostril pair candidates.

Next, the second identification unit 236 detects nostril pair candidatehaving the highest comparison score among the nostril pair candidates asa nostril pair (S222) and ends the processing. The pattern dictionary222 registers an image data obtained by averaging a plurality oftraining normalization image data as a reference image. That is, anaverage value of a plurality of nostril pair image data whose sizes arenormalized as a reference image data.

Next, operation of the authentication apparatus 100 will be described.The face authenticating unit 106 extracts a face region based on thenose tip and nostril information obtained in the identificationapparatus 200. In this case, since the inclination of the face andorientation thereof can be inferred from analogy using the nose tip andnostril information, the face region can be extracted more accurately.

Next, the face authenticating unit 106 calculates feature values to beused for authentication from the face region and compares the featurevalues with feature values registered with the dictionary. When acomparison score exceeds a predetermined value, the face authenticatingunit 106 determines that the face to be authenticated is a human faceregistered beforehand. On the other hand, when the comparison score isless than a predetermined value, the face authenticating unit 106determines that the face to be authenticated is not registered.

Thus, according to the embodiment, the size of a candidate region isdetermined using parallax information, and it is thereby possible toidentify the candidate region more accurately by reducing influences ofa variation in the position of an object. Moreover, candidate points ofthe nose tip is detected according to the value of curvature of thethree-dimensional curved surface generated based on three-dimensionalcoordinates and a candidate region is extracted according to thecandidate points, and it is thereby possible to more stably extract acandidate region and increase the detection accuracy of the nose tipstill more.

Furthermore, nostril candidates are detected based on shape informationand further nostril candidates are detected based on brightnessinformation and parallax information, and it is thereby possible todetect a nostril candidate from a tilted face, too. For this reason, itis possible to extract more stably a candidate region including anostril pair and further increase the detection accuracy of the nostrilpair. According to the embodiment, the face has been described as anexample of the object, but the face is presented only as an example, andthis is not intended to limit the object to the face.

As described above, according to the identification apparatus 200according to the embodiment, the first extraction section 214 determinesthe size of a candidate region corresponding to candidate points of thenose tip using parallax information in the face region. For this reason,the first acquisition unit 216 can acquire feature values from thecandidate region while reducing influences of a positional variation inthe face region and the first identification unit 218 can preventdeterioration of the identification accuracy of the candidate region dueto a positional variation in the face.

Furthermore, the second extraction section 232 determines the size ofthe candidate region corresponding to nostril pair candidates using theparallax information in the face region. For this reason, the secondacquisition unit 234 can acquire a template from the candidate regionwith influences of a positional variation of the face region reduced,and the second identification unit 236 can prevent deterioration of theidentification accuracy of the candidate region due to a positionalvariation of the face.

At least a part of the identification apparatus and authenticationsystem in the above embodiments may be formed of hardware or software.In the case of software, a program realizing at least a partial functionof the identification apparatus and authentication system may be storedin a recording medium such as a flexible disc, CD-ROM, etc. to be readand executed by a computer. The recording medium is not limited to aremovable medium such as a magnetic disk, optical disk, etc., and may bea fixed-type recording medium such as a hard disk device, memory, etc.

Further, a program realizing at least a partial function of the imageprocessor 1 can be distributed through a communication line (includingradio communication) such as the Internet. Furthermore, this program maybe encrypted, modulated, and compressed to be distributed through awired line or a radio link such as the Internet or through a recordingmedium storing it therein.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel methods and systems describedherein may be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the methods andsystems described herein may be made without departing from the spiritof the inventions. The accompanying claims and their equivalents areintended to cover such forms or modifications as would fall within thescope and spirit of the inventions.

What is claimed is:
 1. An identification apparatus comprising: anextraction unit configured to extract, from an image data, a candidateregion including candidate points of a predetermined object usingparallax information; an acquisition unit configured to acquire acharacteristic value based on image information in the candidate region;and an identification unit configured to identify whether or not thecandidate region includes the predetermined object based on similaritybetween the characteristic value and a reference characteristic value.2. The identification apparatus according to claim 1, furthercomprising: an estimation unit configured to estimate an object regionfrom the image data; and a detection unit configured to transformtwo-dimensional coordinates corresponding to pixels in the object regioninto three-dimensional coordinates using the parallax information anddetects the candidate points according to a curvature value of athree-dimensional curved surface generated based on thethree-dimensional coordinates, wherein the extraction unit extracts thecandidate region based on the candidate points.
 3. The identificationapparatus according to claim 2, wherein the candidate points arethree-dimensional coordinates indicating nose tip candidates, theacquisition unit acquires feature values based on a gradient value inthe candidate region as the characteristic values, and theidentification unit identifies whether or not the candidate regioncorresponds to the nose tip.
 4. The identification apparatus accordingto any one of claims 1 to 3, wherein the extraction unit extracts acandidate region including nostril pair candidates, and theidentification unit identifies whether or not the candidate regioncorresponds to a nostril pair.
 5. The identification apparatus accordingto claim 2, wherein the extraction unit extracts a plurality ofcandidate regions corresponding to a plurality of candidate pointsrespectively, and the identification unit identifies a candidate regionhaving a feature value exhibiting highest similarity with the referencefeature value and designates a candidate point corresponding to thecandidate region as the predetermined object.
 6. The identificationapparatus according to claim 2, wherein the estimation unit divides theimage into a foreground region and a background region using theparallax information and estimates the foreground region in apredetermined range as an object region.
 7. The identification apparatusaccording to claim 2, wherein the detection unit transforms a rangepredetermined based on the predetermined object into thethree-dimensional curved surface.
 8. The identification apparatusaccording to claim 7, wherein the detection unit transforms thepredetermined range into the three-dimensional curved surface based onan average value of a parallax corresponding to the object region. 9.The identification apparatus according to claim 8, wherein the detectionunit generates the three-dimensional curved surface usingthree-dimensional coordinates arranged at predetermined intervals amongthe three-dimensional coordinates.
 10. The identification apparatusaccording to claim 4, further comprising: a first nostril candidatedetecting unit configured to detect circular nostril candidates from animage region determined based on a position of the nose tip; a secondnostril candidate detecting unit configured to detect a region in whicha brightness value in the image region is equal to or less than apredetermined value and a parallax is equal to or greater than apredetermined value as a nostril candidate; and a nostril pairprocessing unit configured to designate the nostril candidate as anostril pair candidate, wherein the extraction unit designates a regionbased on the nostril pair candidate as the candidate region.
 11. Theidentification apparatus according to claim 10, wherein the nostril pairprocessing unit designates the nostril candidates as the nostril paircandidates based on an absolute distance between nostril candidates anda positional relationship between the nose tip and nose candidates. 12.An authentication system comprising: an extraction unit configured toextract, from an image data, a candidate region including candidatepoints of a predetermined object using parallax information; anacquisition unit configured to acquire a characteristic value based onimage information in the candidate region; an identification unitconfigured to identify whether or not the candidate region includes thepredetermined object based on similarity between the characteristicvalue and a reference characteristic value; and an authentication unitconfigured to acquire information used for authentication from the imagebased on position information on the predetermined object in a candidateregion including the predetermined object and authenticates whether ornot the object is an object registered beforehand.
 13. Theauthentication system according to claim 12, wherein the image is abrightness image captured by two cameras having predetermined baselengths, and the parallax information is information based on theparallax image obtained using the brightness image captured by the twocameras.
 14. The authentication system according to claim 12, furthercomprising: an estimation unit configured to estimate the object regionfrom the image data; and a detection unit configured to transformtwo-dimensional coordinates corresponding to pixels in the object regioninto three-dimensional coordinates using the parallax information anddetects the candidate points according to a curvature value of athree-dimensional curved surface generated based on thethree-dimensional coordinates, wherein the extraction unit extracts thecandidate region based on the candidate point.
 15. The authenticationsystem according to claim 12, wherein the candidate points arethree-dimensional coordinates indicating nose tip candidates, thedetection unit detects, when the curvature values at thethree-dimensional coordinates are within a predetermined range, thethree-dimensional coordinates as the candidate points, the acquisitionunit acquires feature values based on a gradient value in the candidateregion as the characteristic values, and the identification unitidentifies whether or not the candidate region corresponds to the nosetip.
 16. The authentication system according to claim 12, wherein theextraction unit extracts a candidate region including nostril paircandidates, and the identification unit identifies whether or not thecandidate region corresponds to a nostril pair.
 17. The authenticationsystem according to claim 14, wherein the extraction unit extracts aplurality of candidate regions corresponding to a plurality of candidatepoints respectively, and the identification unit identifies a candidateregion having a feature value exhibiting highest similarity with thereference feature value and designates a candidate point correspondingto the candidate region as the predetermined object.
 18. Theauthentication system according to claim 14, wherein the estimation unitdivides the image into a foreground region and a background region usingthe parallax information and estimates the foreground region in apredetermined range as an object region.
 19. The authentication systemaccording to claim 14, wherein the detection unit transforms a rangepredetermined based on the predetermined object into thethree-dimensional curved surface.
 20. An identification methodcomprising: extracting, from a given object region in an image, acandidate region including candidate points of a predetermined objectusing parallax information; acquiring a characteristic value based onimage information in the candidate region; and identifying whether ornot the candidate region includes the predetermined object based onsimilarity between the characteristic value and a referencecharacteristic value.